基于SVRGD的机载预警雷达自适应波束形成算法

doi:10.3969/j.issn.1001-506X.2021.01.11

Abstract

Abstract:

Adaptive beamforming is a key step of digital signal processing in airborne early warning radar. To solve the problem that the beamforming performance of traditional least mean square (LMS) algorithms is reduced under the condition of short snapshot and the algorithm tend to converge to local optimal value because iterative oscillation, an adaptive beamforming approach for stochastic variance reduction gradient descent (SVRGD) based on machine learning is proposed. Firstly, the data model of planar array receiving signal is established. Secondly, based on the stochastic gradient descent principle, the variance reduction method is introduced to modify the gradient through internal and external iteration for reducing the variance of the stochastic gradient estimation, and the algorithm model and implementation process are established. Finally, by setting up a planar array simulation scene, the performance of the SVRGD algorithm in the aspects of beamforming, anti-jamming and convergence speed is analyzed, and the excellent capability of algorithm in the background of short snapshot number, strong interference and noise is verified.

Key words: airborne early warning radar, adaptive beamforming, stochastic gradient descent (SGD), stochastic variance reduction gradient descent (SVRGD), machine learning

CLC Number:

TN959.73

Fang PENG, Jun WU, Shuai WANG, Jianjun XIANG. Adaptive beamforming algorithm for airborne early warning radar based on SVRGD[J]. Systems Engineering and Electronics, 2021, 43(1): 83-90.

Figures/Tables 11

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References 24

1	张良, 祝欢, 杨予昊, 等. 机载预警雷达技术及信号处理方法综述[J]. 电子与信息学报, 2016, 38 (12): 3298- 3306.
	ZHANG L , ZHU H , YANG Y H , et al. Overview on airborne early warning radar technology and signal processing methods[J]. Journal of Electronics & Information Technology, 2016, 38 (12): 3298- 3306.
2	TALISA S H , O'HAVER K W , COMBERIATE T M , et al. Benefits of digital phased array radars[J]. Proceedings of the IEEE, 2016, 104 (3): 530- 543.
3	ECKHARDT J M , NIKO J , ADRIAN F , et al. FMCW multiple-input multiple-output radar with iterative adaptive beamforming[J]. IET Radar, Sonar & Navigation, 2018, 12 (11): 1187- 1195.
4	HU B , WU X C , ZHANG X , et al. Adaptive beamforming based on compressed sensing with gain/phase uncertainties[J]. IEICE Trans.on Fundamentals of Electronics, Communications and Computer Sciences, 2018, 101 (8): 1257- 1262.
5	ASHWINI D , ZALAWADIA K . Performance analysis of LMS adaptive beamforming algorithm for smart antenna system[J]. International Journal of Computer Applications, 2018, 179 (28): 34- 37.
6	SHI W L , LI Y S , YIN J W . Improved constraint NLMS algorithm for sparse adaptive array beamforming control applications[J]. Applied Computational Electromagnetics Society Journal, 2019, 34 (3): 419- 424.
7	杨志伟, 张攀, 陈颖, 等. 导向矢量和协方差矩阵联合迭代估计的稳健波束形成算法[J]. 电子与信息学报, 2018, 40 (12): 2874- 2879.
	YANG Z W , ZHANG P , CHEN Y , et al. Steering vector and covariance matrix joint iterative estimations for robust beamforming[J]. Journal of Electronics & Information Technology, 2018, 40 (12): 2874- 2879.
8	霍立平, 毛兴鹏, 石运梅, 等. 基于最小敏感度的广义线性自适应波束形成算法[J]. 系统工程与电子技术, 2019, 41 (3): 471- 475.
	HUO L P , MAO X P , SHI Y M , et al. Widely linear adaptive beamforming algorithm based on minimum sensitivity[J]. Systems Eggineering and Electronics, 2019, 41 (3): 471- 475.
9	唐寅州, 赵高泽. 改进粒子群优化算法自适应波束形成技术[J]. 舰船科学技术, 2018, 40 (9): 111- 115.
	TANG Y Z , ZHAO G Z . Adaptive beamforming technique based on a modified particle swarm optimization[J]. Ship Science and Technology, 2018, 40 (9): 111- 115.
10	CHAUDHARI P, SOATTO S. Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks[C]//Proc.of the IEEE Information Theory and Applications Workshop, 2018.
11	SHI W L , LI Y S , SUN L J , et al. Norm constrained noise-free algorithm for sparse adaptive array beamforming[J]. Applied Computational Electromagnetics Society Journal, 2019, 34 (5): 709- 715.
12	YAO Z , SAXE A M , ADVANI M S , et al. Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning[J]. Molecular Physics, 2018, 116 (21): 3214- 3223.
13	BOTTARELLI L , LOOG M . Gaussian process variance reduction by location selectior[J]. Pattern Recognition Letters, 2019, 125 (7): 727- 734.
14	LIU S T , JIANG H L , LIU L B , et al. Gradient descent using stochastic circuits for efficient training of learning machines[J]. IEEE Trans.on Computer-Aided Design of Integrated Circuits and Systems, 2018, 37 (11): 2530- 2541.
15	PHILIP C , JIM R , HUBERT D B . Estimating neural sources using a worst-case robust adaptive beamforming approach[J]. Biomedical Signal Processing and Control, 2019, 52, 330- 340.
16	KHAN Z A , CHAUDHARY N I , ZUBAIR S . Fractional stochastic gradient descent for recommender systems[J]. Electronic Markets, 2019, 29 (2): 275- 285.
17	JOHNSON R, ZHANG T. Accelerating stochastic gradient descent using predictive variance reduction[C]//Proc.of the 27th Annual Conference on Neural Information Processing Systems, 2013: 315-323.
18	YANG Z , WANG C , ZHANG Z M , et al. Random Barzilai-Borwein step size for mini-batch algorithms[J]. Engineering Applications of Artificial Intelligence, 2018, 72 (6): 124- 135.
19	MIN E X , LONG J , CUI J J . Analysis of the variance reduction in SVRG and a new acceleration method[J]. IEEE Access, 2018, 4, 16165- 16175.
20	CHEN L , ZHOU S S , ZHANG Z . SVRG for a non-convex problem using graduated optimization algorithm[J]. Journal of Intelligent & Fuzzy Systems, 2018, 4, 34 (1): 153- 165.
21	RAMAZANLI I, NGUYEN H, PHAM H, et al. Adaptive sampling distributed stochastic variance reduced gradient for heterogeneous distributed datasets[J].[2020-06-10]. http://arxiv.org/abs/2002.08528v1.
22	MING Y W , ZHAO Y W , WU C K , et al. Distributed and asynchronous stochastic gradient descent with variance reduction[J]. Neurocomputing, 2018, 281, 27- 36.
23	YANG Z , WANG C , ZHANG Z M , et al. Accelerated stochastic gradient descent with step size selection rules[J]. Signal processing, 2019, 159 (6): 171- 186.
24	LUO Z J , QIAN Y T . Stochastic sub-sampled Newton method with variance reduction[J]. International Journal of Wavelets, Multiresolution and Information Processing, 2019, 17 (6): 62- 68.

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[8]	TANG Min, QI Dong, LIU Chengcheng, ZHAO Yongjun. Adaptive beamforming algorithm for coherent signal in single snapshot case [J]. Systems Engineering and Electronics, 2019, 41(6): 1224-1229.
[9]	HUO Liping, MAO Xingpeng, SHI Yunmei, JIA Shaowen, LI Guangyan. Widely linear adaptive beamforming algorithm based on minimum sensitivity [J]. Systems Engineering and Electronics, 2019, 41(3): 471-475.
[10]	ZHANG Jing, HE You, PENG Yingning, LI Gang. Technical features demonstration of the next generation fighter [J]. Systems Engineering and Electronics, 2018, 40(8): 1754-1759.
[11]	ZHUANSUN Shaoshuai, YANG Junan, LIU Hui, HUANG Keju. Jamming strategy learning based on positive reinforcement learning and orthogonal decomposition [J]. Systems Engineering and Electronics, 2018, 40(3): 518-525.
[12]	WANG Yi, SUN Jin-biao, XIAO Ming-qing, LUO Ji-xun. Research of dynamic weapon-target assignment problem based on type-2 interval fuzzy K-nearest neighbors classifier [J]. Systems Engineering and Electronics, 2016, 38(6): 1314-1319.
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[15]	WANG Xian-chao, TANG Zi-yue, ZHU Zhen-bo, LIAN Xiao-feng. Flight planning of low-altitude penetration based on Doppler blind zone [J]. Systems Engineering and Electronics, 2015, 37(7): 1531-1535.

Adaptive beamforming algorithm for airborne early warning radar based on SVRGD

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